agulli/atlas-agents

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This repository contains the source code, examples, and project implementations for the book — a practical, code-first guide to building AI agents and, more importantly, putting them into production.

Overview

README

Atlas Agents — Hands-on AI Agents in Production

This repository contains the source code, examples, and project implementations for the book “Hands-on AI Agents” — a practical, code-first guide to building AI agents and, more importantly, putting them into production.

🚀 Book Overview

Where Agentic Design Patterns taught how to write agents, this book is about how to run them: the harnesses, guardrails, evals, loops, and deployment machinery that turn a demo into a system you can trust unattended. One project — Atlas, an autonomous engineering assistant — grows chapter by chapter from a fifty-line ReAct loop into a self-correcting, self-improving production system.

Key Technologies

Claude API / OpenAI / Gemini: multi-provider agent cores with structured outputs.
LangGraph & CrewAI: stateful agent graphs and role-based multi-agent orchestration.
MCP & A2A: universal tool connectivity and agent-to-agent discovery.
Claude Code & Antigravity: agentic coding harnesses and loop primitives.
Agent Skills: declarative, progressively-disclosed expertise (SKILL.md).
E2B / Docker sandboxes: safe code execution boundaries.
Managed Agents: server-run sessions, outcomes, and scheduled deployments.
LiteLLM, DSPy, Ollama: model portability, routing, and local inference.

📁 Repository Structure

Each chapter folder holds the chapter’s Atlas project. Extended examples that go beyond the printed text live in each chapter’s online/ subfolder.

Folder	Chapter
`ch01_react_from_scratch/`	Anatomy of an Agent — the minimal ReAct loop
`ch02_prompt_architecture/`	Prompt Architecture for Agents
`ch03_tools_and_skills/`	Tools, Skills, and Structured Outputs
`ch04_handoffs/`	Handoffs and Routines — the support triage router
`ch05_state_graphs/`	Stateful Agent Graphs — LangGraph persistence and HITL
`ch06_multi_agent/`	Multi-Agent Collaboration — CrewAI and debate protocols
`ch07_model_portability/`	One Agent, Many Models — LiteLLM, Ollama, DSPy
`ch08_mcp_a2a/`	Open Protocols — MCP servers and A2A discovery
`ch09_agent_skills/`	Agent Skills — the production skill library
`ch10_claude_code_antigravity/`	Claude Code and Antigravity
`ch11_memory/`	Memory and Agentic RAG
`ch12_sandboxes/`	Code Execution and Sandbox Agents
`ch13_multimodal/`	Multimodal and Voice Agents
`ch14_guardrails/`	Guardrails and Agent Safety
`ch15_agent_harness/`	Agent Harness Engineering
`ch16_always_on_agents/`	Always-On Agents — daemons, watchdogs, recovery
`ch17_managed_agents/`	Managed Agents — let the platform run it
`ch18_evaluation/`	Evaluation and Observability
`ch19_deployment/`	Deployment, Async Agents, and Security
`ch20_loop_engineering/`	Loop Engineering — the self-correcting fix loop
`ch21_harness_engineer/`	The Harness Engineer — /learn, adversarial pairs, prose verifiers
`ch22_capstone/`	Capstone: Atlas — the Autonomous Engineering Assistant
`ch23_future/`	What’s Next — scaffold optimization and reasoning benchmarks
`shared/`	Global config and declarative skill models used across chapters

🛠️ Prerequisites

Python 3.10+
API keys as needed per chapter: Anthropic, OpenAI, Google Gemini (see shared/config.py — keys load from a .env at the repo root)
pip install -r requirements.txt (per-chapter extras are noted in each file’s header)
Basic understanding of LLM prompting and Python

▶️ Running the Examples

Every script is self-contained and documents its own usage and dependencies in its module docstring:

cd ch20_loop_engineering
python fix_loop.py --repo ./orders-service --goal "pytest green, ruff clean"

View this README on GitHub

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Install

npx skillfish add agulli/atlas-agents